Jonathan H. Huggins

发表

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees

Trevor Campbell, Tamara Broderick, Jonathan H. Huggins, 2018, AISTATS.

Robust, Accurate Stochastic Optimization for Variational Inference pdf

Aki Vehtari, Michael Riis Andersen, Alejandro Catalina, 2020, NeurIPS.

Coresets for Scalable Bayesian Logistic Regression

Trevor Campbell, Tamara Broderick, Jonathan H. Huggins, 2016, NIPS.

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

Ryan P. Adams, Tamara Broderick, Jonathan H. Huggins, 2017, NIPS.

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes pdf

Ardavan Saeedi, Vikash K. Mansinghka, Karthik Narasimhan, 2015, ICML.

Random Feature Stein Discrepancies

Lester Mackey, Jonathan H. Huggins, Lester W. Mackey, 2018, NeurIPS.

Validated Variational Inference via Practical Posterior Error Bounds

Trevor Campbell, Tamara Broderick, Jonathan H. Huggins, 2019, AISTATS.

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions pdf

Tamara Broderick, Raj Agrawal, Jonathan H. Huggins, 2019, ICML.

Robust Inference and Model Criticism Using Bagged Posteriors

Jeffrey W. Miller, Jonathan H. Huggins, 2019 .

Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data pdf

Trevor Campbell, William T. Stephenson, Tamara Broderick, 2018, ArXiv.

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models pdf

Matthew J. Johnson, Ardavan Saeedi, Jonathan H. Huggins, 2015, ArXiv.

A Statistical Learning Theory Framework for Supervised Pattern Discovery

Cynthia Rudin, Jonathan H. Huggins, C. Rudin, 2013, SDM.

Robust and Reproducible Model Selection Using Bagged Posteriors

Jeffrey W. Miller, Jonathan H. Huggins, 2020 .

An information-theoretic analysis of resampling in Sequential Monte Carlo

Jonathan H. Huggins, 2014 .

Data-dependent compression of random features for large-scale kernel approximation

Trevor Campbell, Tamara Broderick, Raj Agrawal, 2019, AISTATS.

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach pdf

Trevor Campbell, Tamara Broderick, Jonathan H. Huggins, 2018, ArXiv.

Risk and Regret of Hierarchical Bayesian Learners pdf

Joshua B. Tenenbaum, Jonathan H. Huggins, J. Tenenbaum, 2015, ICML.

Toward a Theory of Pattern Discovery pdf

Cynthia Rudin, Jonathan H. Huggins, C. Rudin, 2014, ISAIM.

Using bagged posteriors for robust inference and model criticism

Jeffrey W. Miller, Jonathan H. Huggins, Jeffrey W. Miller, 2019, 1912.07104.

Practical Posterior Error Bounds from Variational Objectives pdf

Trevor Campbell, Tamara Broderick, Jonathan H. Huggins, 2019, ArXiv.

Scaling Bayesian inference: theoretical foundations and practical methods

Jonathan H. Huggins, Jonathan Huggins, 2018 .

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations pdf

Tamara Broderick, Raj Agrawal, Jonathan H. Huggins, 2019, ICML.

The feasibility of targeted test-trace-isolate for the control of SARS-CoV-2 variants

Jonathan H. Huggins, Alun L. Lloyd, Kevin M. Esvelt, 2021, F1000Research.

Sequential Monte Carlo as approximate sampling: bounds, adaptive resampling via $\infty$-ESS, and an application to particle Gibbs

Daniel M. Roy, Jonathan H. Huggins, Jonathan Huggins, 2015, Bernoulli.

Provably Learning Mixtures of Gaussians and More

Jonathan H. Huggins, 2010 .

Bidirectional contact tracing dramatically improves COVID-19 control

Jonathan H. Huggins, Alun L. Lloyd, Ethan C. Alley, 2020 .